Design, Analysis, and Interpretation of Genome-Wide Association Scans

  • Daniel O.┬áStram
Part of the Statistics for Biology and Health book series (SBH)

Table of contents

  1. Front Matter
    Pages i-xv
  2. Daniel O. Stram
    Pages 1-29
  3. Daniel O. Stram
    Pages 31-77
  4. Daniel O. Stram
    Pages 79-133
  5. Daniel O. Stram
    Pages 183-211
  6. Daniel O. Stram
    Pages 213-242
  7. Daniel O. Stram
    Pages 285-327
  8. Back Matter
    Pages 329-334

About this book

Introduction

This book presents the statistical aspects of designing, analyzing and interpreting the results of genome-wide association scans (GWAS studies) for genetic causes of disease using unrelated subjects. Particular detail is given to the practical aspects of employing the bioinformatics and data handling methods necessary to prepare data for statistical analysis. The goal in writing this book is to give statisticians, epidemiologists, and students in these fields the tools to design a powerful genome-wide study based on current technology. The other part of this is showing readers how to conduct analysis of the created study.

Design and Analysis of Genome-Wide Association Studies provides a compendium of well-established statistical methods based upon single SNP associations. It also provides an introduction to more advanced statistical methods and issues. Knowing that technology, for instance large scale SNP arrays, is quickly changing, this text has significant lessons for future use with sequencing data. Emphasis on statistical concepts that apply to the problem of finding disease associations irrespective of the technology ensures its future applications. The author includes current bioinformatics tools while outlining the tools that will be required for use with extensive databases from future large scale sequencing projects. The author includes current bioinformatics tools while outlining additional issues and needs arising from the extensive databases from future large scale sequencing projects.

Keywords

Biostatistics GWAS studies Genetic disease variation Genome studies Risk prediction Statistics in public health

Authors and affiliations

  • Daniel O.┬áStram
    • 1
  1. 1.Dept. Preventive MedicineUniversity of Southern California Keck School of MedicineLos AngelesUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4614-9443-0
  • Copyright Information Springer Science+Business Media New York 2014
  • Publisher Name Springer, New York, NY
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-1-4614-9442-3
  • Online ISBN 978-1-4614-9443-0
  • Series Print ISSN 1431-8776
  • Series Online ISSN 2197-5671
  • About this book